Abstract
Road defect detection is a key task to ensure road safety and repair damage in a timely manner. However, traditional manual inspection methods are inefficient and costly. To address such problems, a BL-YOLOv8 enhanced road defect detection algorithm is proposed. By integrating the BiFPN concept, the neck structure of the YOLOv8s model is reconstructed, reducing model parameters, computational load and overall size, and introducing a dynamic large convolution kernel attention mechanism (LSK-attention) to expand the model's receptive field and improve the accuracy of target detection. The experimental results show that on the road defect data set (KITTI), the number of parameters is reduced by 23.03 %, the amount of calculation is reduced by 5.85 %, and the average accuracy of mAP@0.5 is increased by 2.1 %, verifying the effectiveness of this method for automatic road defect detection technology effectiveness.
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